Currently, the cluster method based on user interest has been widely used in recommendations of web pages or advertisements, such that users can find interested web page contents or view interested commodities quickly.
Before accomplishing the cluster of user interest, it is necessary to find and make statistics of user interest. The current method of finding user interest is generally achieved by making statistics of the various operation data of web pages, such as opening times of a web page, content information of the searched products and category information of purchased products, then converting statistical data into weight values of corresponding user interest.
However, this method has a problem in terms of the cluster of user interest. Because only web page behavior data of a user is considered, if a user has not generated any behavior data before, interest of the user cannot be determined, the cluster operation cannot be performed any further, and thus the content recommended to the user would be inaccurate.